function [Shuffled_RM_Structure] = Pull_Out_Shuffled_RM_Single_Session(Data_Summary, Row_Number)

% pull out the shuffled RM values first
RM_shuffle = Data_Summary(Row_Number).Shuffled_Rate_Map_Corr_values;
RM_shuffle_values = RM_shuffle(:,:,1);
RM_shuffle_values = RM_shuffle_values(:);

% make a structure that summarizes useful experimental conditions
Shuffled_RM_Structure_Original = struct(...
    'session', Row_Number,...
    'genotype', Data_Summary(Row_Number).genotype,...
    'animal_ID', Data_Summary(Row_Number).animalName,...
    'expt_date', Data_Summary(Row_Number).dateToFind,...
    'drug', Data_Summary(Row_Number).drug,...
    'session1_dose', Data_Summary(Row_Number).session1_dose,...
    'session2_dose', Data_Summary(Row_Number).session2_dose,...
    'exptParadigm', Data_Summary(Row_Number).exptParadigm,...
    'session1_mobility_pass', Data_Summary(Row_Number).Mobility_Session1_Pass,...
    'session2_mobility_pass', Data_Summary(Row_Number).Mobility_Session2_Pass);
Shuffled_RM_Structure = Shuffled_RM_Structure_Original;

% duplicate this structure for N times, where N is the number of non-NAN RM correlation values in
% that session
for n = 1:(size(RM_shuffle_values,1)-1)
    Shuffled_RM_Structure = cat(1, Shuffled_RM_Structure, Shuffled_RM_Structure_Original);
end

% Now put all RM values into that structure
for n = 1:size(Shuffled_RM_Structure,1)
    Shuffled_RM_Structure(n).Shuffled_RM_values = RM_shuffle_values(n,1);
end


end